Literature DB >> 24195665

Recent advances in multidimensional QSAR (4D-6D): a critical review.

Manoj G Damale, Sanjay N Harke, Firoz A Kalam Khan, Devanand B Shinde, Jaiprakash N Sangshetti1.   

Abstract

The quantitative structure activity relationship (QSAR) study is the most cited and reliable computational technique used for decades to obtain information about a substituent's physicochemical property and biological activity. There is step-by-step development in the concept of QSAR from 0D to 2D. These models suffer various limitations that led to the development of 3D-QSAR. There are large numbers of literatures available on the utility of 3D-QSAR for drug design. Three-dimensional properties of molecules with non-covalent interactions are served as important tool in the selection of bioactive confirmation of compounds. With this view, 3D-QSAR has been explored with different advancements like COMFA, COMSA, COMMA, etc. Some reports are also available highlighting the limitations of 3D-QSAR. In a way, to overcome the limitations of 3D-QSAR, more advanced QSAR approaches like 4D, 5D and 6D-QSAR have been evolved. Here, in this present review we have focused more on the present and future of more predictive models of QSAR studies. The review highlights the basics of 3D to 6D-QSAR and mainly emphasizes the advantages of one dimension over the other. It covers almost all recent reports of all these multidimensional QSAR approaches which are new paradigms in drug discovery.

Mesh:

Year:  2014        PMID: 24195665     DOI: 10.2174/13895575113136660104

Source DB:  PubMed          Journal:  Mini Rev Med Chem        ISSN: 1389-5575            Impact factor:   3.862


  12 in total

1.  Application of 4D-QSAR studies to a series of benzothiophene analogs.

Authors:  Giovana Baptista Caldas; Teodorico C Ramalho; Elaine F F da Cunha
Journal:  J Mol Model       Date:  2014-09-16       Impact factor: 1.810

Review 2.  In Silico Studies in Drug Research Against Neurodegenerative Diseases.

Authors:  Farahnaz Rezaei Makhouri; Jahan B Ghasemi
Journal:  Curr Neuropharmacol       Date:  2018       Impact factor: 7.363

3.  Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules.

Authors:  Andrei L Lomize; Irina D Pogozheva
Journal:  J Chem Inf Model       Date:  2019-07-01       Impact factor: 4.956

4.  A multilevel approach for screening natural compounds as an antiviral agent for COVID-19.

Authors:  Mahdi Vasighi; Julia Romanova; Miroslava Nedyalkova
Journal:  Comput Biol Chem       Date:  2022-05-11       Impact factor: 3.737

5.  Comparative molecular field analysis and molecular dynamics studies of α/β hydrolase domain containing 6 (ABHD6) inhibitors.

Authors:  Agnieszka A Kaczor; Katarzyna M Targowska-Duda; Jayendra Z Patel; Tuomo Laitinen; Teija Parkkari; Yahaya Adams; Tapio J Nevalainen; Antti Poso
Journal:  J Mol Model       Date:  2015-09-08       Impact factor: 1.810

6.  Comparative molecular field analysis and molecular dynamics studies of the dopamine D2 receptor antagonists without a protonatable nitrogen atom.

Authors:  Agnieszka A Kaczor; Justyna Żuk; Dariusz Matosiuk
Journal:  Med Chem Res       Date:  2018-02-13       Impact factor: 1.965

7.  Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations.

Authors:  Shiv Bharadwaj; Amit Dubey; Nitin Kumar Kamboj; Amaresh Kumar Sahoo; Sang Gu Kang; Umesh Yadava
Journal:  Sci Rep       Date:  2021-05-13       Impact factor: 4.379

Review 8.  Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design.

Authors:  Sebastjan Kralj; Marko Jukič; Urban Bren
Journal:  Int J Mol Sci       Date:  2021-12-30       Impact factor: 5.923

9.  A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.

Authors:  Yasunari Matsuzaka; Yoshihiro Uesawa
Journal:  Int J Mol Sci       Date:  2022-02-15       Impact factor: 5.923

10.  3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors.

Authors:  Yajing Fang; Yulin Lu; Xixi Zang; Ting Wu; XiaoJuan Qi; Siyi Pan; Xiaoyun Xu
Journal:  Sci Rep       Date:  2016-04-06       Impact factor: 4.379

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